An entity, such as enterprise, may want to analyze or “mine” large amounts of data, such as text data. For example, an enterprise might want to analyze tens of thousands of text files to look for patterns (e.g., so that predictions can be made and/or resources may be allocated in appropriate ways). Note that an entity might analyze this data in connection with different purposes, and, moreover, different purposes may need to analyze the data in different ways. For example, a single acronym might refer to one thing when it appears in one type of document and different thing when it appears in a different type of document. It can be difficult to identify patterns across such large amounts of data and different purposes. In addition, manually managing the different needs and requirements (e.g., different logic rules) associated with different purposes can be a time consuming and error prone process.
Note that electronic records may be used to store information for an enterprise. Moreover, it may be advantageous for an enterprise to accurately assign a credibility value to various views of the data. For example, a subset of the electronic records (e.g., filtered based on one or more keywords discovered during a text-based analysis of the data) might be used to generate a view of the data at a particular level of granularity. In some cases, however, the amount of information available in the electronic records in connection with a particular level of granularity might be too small to be statistically meaningful. For example, if only one or two (or even zero) records exist having the keyword at a particular level of granularity, it might not be possible to draw meaningful conclusions about the overall usefulness of the information for the enterprise. In some cases, an enterprise might be interested in quantifying how unique a particular outcome will be as compared to an outcome deemed as “typical.” As used herein, this value may be referred to as “credibility.”
An enterprise may use credible information to help properly allocate resources, plan for future events, etc. Thus, there is a need in the art for methods and systems using text mining to properly assign credibility weightings for electronic records. In addition, there is a need in the art for methods and systems of addressing these values.